library(coda)
## Warning: package 'coda' was built under R version 3.5.2
library(lattice)

2R hum

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/1.hum.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed          1      0.6227 
## Fst2  passed          1      0.9859 
## Fst3  passed          1      0.7856 
## Fst4  passed          1      0.3505 
## Fst5  passed          1      0.8749 
## Fst6  passed          1      0.0843 
## Fst7  passed          1      0.1021 
## Fst8  passed          1      0.9269 
## Fst9  passed          1      0.1180 
## Fst10 passed          1      0.5531 
## Fst11 passed          1      0.7365 
## Fst12 passed       2001      0.1756 
## Fst13 passed          1      0.2443 
## Fst14 passed          1      0.9299 
##                                 
##       Halfwidth Mean   Halfwidth
##       test                      
## Fst1  passed    0.0603 6.67e-06 
## Fst2  passed    0.0241 4.27e-06 
## Fst3  passed    0.0364 5.00e-06 
## Fst4  passed    0.0294 3.95e-06 
## Fst5  passed    0.0358 5.96e-06 
## Fst6  passed    0.0300 5.37e-06 
## Fst7  passed    0.0432 4.50e-06 
## Fst8  passed    0.0569 5.18e-06 
## Fst9  passed    0.0208 3.02e-06 
## Fst10 passed    0.0208 3.02e-06 
## Fst11 passed    0.0622 6.16e-06 
## Fst12 passed    0.4568 3.17e-05 
## Fst13 passed    0.1145 1.08e-05 
## Fst14 passed    0.0249 3.48e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 3271.006 2488.018 2856.687 3655.435 4303.057 4209.196 3968.223 3708.013 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 3101.758 2979.385 2951.396 4607.163 3812.188 3486.062
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                 Fst1         Fst2         Fst3          Fst4         Fst5
## Lag 0    1.000000000  1.000000000  1.000000000  1.0000000000  1.000000000
## Lag 10   0.168784606  0.255240332  0.227968359  0.1551481489  0.074716539
## Lag 50  -0.007916957  0.037564529  0.010855904 -0.0171478564  0.003740783
## Lag 100  0.012401647 -0.006469221 -0.005723599 -0.0121210717  0.022491267
## Lag 500 -0.015879618  0.002322632  0.027177250 -0.0006427306 -0.009042370
##                Fst6          Fst7        Fst8        Fst9       Fst10
## Lag 0    1.00000000  1.0000000000  1.00000000 1.000000000  1.00000000
## Lag 10   0.08567255  0.1148507177  0.14817192 0.214893448  0.20845917
## Lag 50   0.01154717  0.0070821780  0.00930051 0.005278132  0.03048233
## Lag 100 -0.01490612 -0.0086222395 -0.01589813 0.001851678 -0.01765533
## Lag 500  0.01558492 -0.0007983081  0.02672486 0.013587178 -0.01070272
##              Fst11      Fst12        Fst13        Fst14
## Lag 0   1.00000000 1.00000000  1.000000000  1.000000000
## Lag 10  0.19641526 0.04069025  0.104253819  0.178209216
## Lag 50  0.00173944 0.02478804 -0.004895669  0.011650920
## Lag 100 0.01112094 0.01683631 -0.003520598 -0.020390577
## Lag 500 0.01523973 0.01157483 -0.010172876  0.000879831
levelplot(t(autocorr.diag(chain)))

plot(chain)

2L hum

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/2.hum.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed       1         0.0551 
## Fst2  passed       1         0.2781 
## Fst3  passed       1         0.1969 
## Fst4  passed       1         0.0606 
## Fst5  passed       1         0.4148 
## Fst6  passed       1         0.1648 
## Fst7  passed       1         0.9944 
## Fst8  passed       1         0.0991 
## Fst9  passed       1         0.9120 
## Fst10 passed       1         0.8679 
## Fst11 passed       1         0.6065 
## Fst12 passed       1         0.4698 
## Fst13 passed       1         0.1486 
## Fst14 passed       1         0.7841 
##                                 
##       Halfwidth Mean   Halfwidth
##       test                      
## Fst1  passed    0.1228 1.42e-05 
## Fst2  passed    0.0931 1.08e-05 
## Fst3  passed    0.0917 1.13e-05 
## Fst4  passed    0.0375 5.96e-06 
## Fst5  passed    0.0267 6.94e-06 
## Fst6  passed    0.0303 7.53e-06 
## Fst7  passed    0.0334 5.30e-06 
## Fst8  passed    0.0580 6.48e-06 
## Fst9  passed    0.0145 3.50e-06 
## Fst10 passed    0.0421 6.63e-06 
## Fst11 passed    0.0799 8.56e-06 
## Fst12 passed    0.4373 3.10e-05 
## Fst13 passed    0.1574 1.61e-05 
## Fst14 passed    0.0366 6.35e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 3319.051 3508.119 3165.469 3398.867 3783.645 3284.620 3249.030 3663.860 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 2425.672 2681.358 3445.515 4370.792 3894.237 2722.635
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                Fst1         Fst2          Fst3         Fst4        Fst5
## Lag 0   1.000000000  1.000000000  1.0000000000  1.000000000  1.00000000
## Lag 10  0.174564536  0.158310388  0.1841596014  0.168331716  0.10668348
## Lag 50  0.006351562  0.008179829  0.0140239275  0.020503993 -0.01742444
## Lag 100 0.007170402  0.003311487 -0.0021648969 -0.002499203 -0.00081555
## Lag 500 0.016639670 -0.011883007 -0.0004225282  0.022055519 -0.02012081
##                Fst6        Fst7         Fst8         Fst9         Fst10
## Lag 0   1.000000000 1.000000000  1.000000000  1.000000000  1.0000000000
## Lag 10  0.141457806 0.169048215  0.133315654  0.298429353  0.2586503779
## Lag 50  0.005323411 0.013989568  0.018976475  0.001915693  0.0003659035
## Lag 100 0.014505671 0.004983617  0.019248397  0.014203885  0.0033529302
## Lag 500 0.001492427 0.002319752 -0.003511402 -0.002219178 -0.0313196795
##                Fst11        Fst12        Fst13       Fst14
## Lag 0    1.000000000  1.000000000  1.000000000  1.00000000
## Lag 10   0.183867154  0.066946498  0.124126574  0.25367759
## Lag 50   0.001368993  0.008825577 -0.005861448  0.01390425
## Lag 100  0.013602668 -0.012625400 -0.011076151 -0.01628005
## Lag 500 -0.005401785  0.003347106 -0.010215184 -0.01558191
levelplot(t(autocorr.diag(chain)))

plot(chain)

3R hum

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/3.hum.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed       1         0.314  
## Fst2  passed       1         0.551  
## Fst3  passed       1         0.987  
## Fst4  passed       1         0.842  
## Fst5  passed       1         0.556  
## Fst6  passed       1         0.508  
## Fst7  passed       1         0.180  
## Fst8  passed       1         0.146  
## Fst9  passed       1         0.626  
## Fst10 passed       1         0.305  
## Fst11 passed       1         0.875  
## Fst12 passed       1         0.371  
## Fst13 passed       1         0.822  
## Fst14 passed       1         0.955  
##                                  
##       Halfwidth Mean    Halfwidth
##       test                       
## Fst1  passed    0.02045 3.79e-06 
## Fst2  passed    0.00516 2.03e-06 
## Fst3  passed    0.00889 2.32e-06 
## Fst4  passed    0.01768 2.92e-06 
## Fst5  passed    0.02267 5.25e-06 
## Fst6  passed    0.02112 4.77e-06 
## Fst7  passed    0.01783 2.97e-06 
## Fst8  passed    0.05939 5.34e-06 
## Fst9  passed    0.01155 2.41e-06 
## Fst10 passed    0.01045 1.97e-06 
## Fst11 passed    0.03979 4.44e-06 
## Fst12 passed    0.26592 2.38e-05 
## Fst13 passed    0.08136 8.55e-06 
## Fst14 passed    0.01630 2.73e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 2923.514 2799.283 2870.184 4021.534 3917.821 4217.814 3288.256 4040.331 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 2577.361 2960.573 3434.629 4004.605 4124.690 3594.867
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                 Fst1        Fst2         Fst3         Fst4         Fst5
## Lag 0    1.000000000  1.00000000  1.000000000  1.000000000  1.000000000
## Lag 10   0.232074738  0.21373674  0.221548039  0.108261194  0.082143906
## Lag 50   0.025223779  0.02346283  0.008461970 -0.001510485  0.017676703
## Lag 100 -0.009140293  0.01248846 -0.002674830 -0.005365285 -0.004040461
## Lag 500 -0.000246087 -0.01604042 -0.004852505  0.038041243  0.001206352
##                 Fst6         Fst7         Fst8         Fst9        Fst10
## Lag 0    1.000000000 1.0000000000  1.000000000  1.000000000  1.000000000
## Lag 10   0.084657304 0.1462077889  0.105956390  0.207964306  0.207403650
## Lag 50  -0.008843852 0.0193159532  0.007586496  0.042110680  0.001357519
## Lag 100  0.029043640 0.0003761396 -0.019714060  0.005080454 -0.025156367
## Lag 500  0.014482301 0.0037913387  0.002370540 -0.001676296  0.010382988
##                Fst11         Fst12        Fst13        Fst14
## Lag 0    1.000000000  1.0000000000  1.000000000  1.000000000
## Lag 10   0.144757323  0.0753701719  0.075541177  0.135209292
## Lag 50  -0.002783186  0.0245059081 -0.003630617 -0.007007132
## Lag 100 -0.004061852 -0.0071578618  0.021155032 -0.007259280
## Lag 500 -0.017582142 -0.0003327718  0.019381872 -0.006433374
levelplot(t(autocorr.diag(chain)))

plot(chain)

3L hum

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/4.hum.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed          1      0.5413 
## Fst2  passed          1      0.9092 
## Fst3  passed        501      0.1222 
## Fst4  passed          1      0.8131 
## Fst5  passed       1001      0.0998 
## Fst6  passed          1      0.4163 
## Fst7  passed          1      0.7344 
## Fst8  passed          1      0.3577 
## Fst9  failed         NA      0.0211 
## Fst10 passed          1      0.0774 
## Fst11 passed          1      0.3875 
## Fst12 passed          1      0.9676 
## Fst13 passed          1      0.6234 
## Fst14 passed          1      0.9466 
##                                  
##       Halfwidth Mean    Halfwidth
##       test                       
## Fst1  passed    0.02242 4.56e-06 
## Fst2  passed    0.00639 2.28e-06 
## Fst3  passed    0.01104 3.10e-06 
## Fst4  passed    0.01350 3.08e-06 
## Fst5  passed    0.01487 5.61e-06 
## Fst6  passed    0.01387 4.78e-06 
## Fst7  passed    0.01800 3.29e-06 
## Fst8  passed    0.05464 6.08e-06 
## Fst9  <NA>           NA       NA 
## Fst10 passed    0.00760 1.80e-06 
## Fst11 passed    0.03774 5.19e-06 
## Fst12 passed    0.25382 2.54e-05 
## Fst13 passed    0.08533 1.00e-05 
## Fst14 passed    0.01630 3.04e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 3060.150 3036.197 2933.150 3441.250 4555.217 4320.351 3591.420 3904.154 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 3090.127 3447.192 3027.765 4356.104 4280.474 3823.150
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                  Fst1          Fst2         Fst3         Fst4         Fst5
## Lag 0    1.0000000000  1.0000000000  1.000000000  1.000000000  1.000000000
## Lag 10   0.1724964847  0.1707428049  0.218965215  0.143137600  0.046349081
## Lag 50  -0.0175224727 -0.0002878109  0.019876502  0.006047537  0.004111728
## Lag 100 -0.0005221605  0.0136453874 -0.004123944  0.014990245  0.020166019
## Lag 500  0.0232963192  0.0093726180 -0.003757280 -0.024932951 -0.024865619
##                 Fst6         Fst7         Fst8        Fst9        Fst10
## Lag 0    1.000000000  1.000000000  1.000000000  1.00000000  1.000000000
## Lag 10   0.072721935  0.142935212  0.122874260  0.21299296  0.183631983
## Lag 50  -0.001802276 -0.004918926 -0.011906069  0.01491150  0.017119024
## Lag 100  0.015557672  0.019538139  0.011565133  0.01315479  0.001341083
## Lag 500  0.009532110  0.023931592 -0.006159467 -0.00990996 -0.009589162
##                Fst11        Fst12       Fst13         Fst14
## Lag 0    1.000000000  1.000000000 1.000000000  1.0000000000
## Lag 10   0.158532700  0.068621822 0.077332360  0.1026402424
## Lag 50   0.012495780 -0.004198612 0.008781246 -0.0090920412
## Lag 100 -0.008764265  0.004186153 0.022839601 -0.0079769778
## Lag 500 -0.000552086 -0.001577513 0.003687224 -0.0006713689
levelplot(t(autocorr.diag(chain)))

plot(chain)

X hum

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/5.hum.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed          1      0.3292 
## Fst2  passed          1      0.4690 
## Fst3  passed          1      0.4131 
## Fst4  passed          1      0.5568 
## Fst5  passed          1      0.5745 
## Fst6  passed          1      0.9732 
## Fst7  passed       1001      0.1025 
## Fst8  passed          1      0.1827 
## Fst9  passed        501      0.0645 
## Fst10 passed          1      0.7055 
## Fst11 passed          1      0.7816 
## Fst12 passed          1      0.6587 
## Fst13 passed          1      0.1275 
## Fst14 passed          1      0.4689 
##                                 
##       Halfwidth Mean   Halfwidth
##       test                      
## Fst1  passed    0.0400 1.06e-05 
## Fst2  passed    0.0156 5.97e-06 
## Fst3  passed    0.0208 6.90e-06 
## Fst4  passed    0.0186 5.11e-06 
## Fst5  passed    0.0234 7.84e-06 
## Fst6  passed    0.0238 7.51e-06 
## Fst7  passed    0.0102 4.10e-06 
## Fst8  passed    0.0710 1.09e-05 
## Fst9  passed    0.0173 4.91e-06 
## Fst10 passed    0.0157 4.14e-06 
## Fst11 passed    0.0505 9.84e-06 
## Fst12 passed    0.3482 4.47e-05 
## Fst13 passed    0.2261 2.98e-05 
## Fst14 passed    0.0378 7.96e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 2646.689 2254.656 2259.754 3481.653 4437.453 4573.730 3215.727 3705.813 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 3306.769 3245.737 2921.445 4126.123 4355.666 3507.794
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                Fst1         Fst2       Fst3          Fst4         Fst5
## Lag 0    1.00000000  1.000000000 1.00000000  1.0000000000  1.000000000
## Lag 10   0.24847485  0.262736801 0.30117751  0.1361696643  0.059408522
## Lag 50  -0.01880866  0.031891540 0.02502463 -0.0079106499  0.004686054
## Lag 100  0.01677790 -0.008283256 0.02281189 -0.0006298801 -0.004081893
## Lag 500  0.01296741 -0.003054415 0.01750167 -0.0110047670 -0.022643676
##                 Fst6         Fst7         Fst8         Fst9         Fst10
## Lag 0    1.000000000  1.000000000  1.000000000 1.0000000000  1.0000000000
## Lag 10   0.050868392  0.185705990  0.095838175 0.1550192305  0.1795805506
## Lag 50  -0.005768355 -0.005642589 -0.007953327 0.0169562307  0.0002919287
## Lag 100  0.008025088 -0.014638226  0.009838974 0.0004442406 -0.0247072424
## Lag 500 -0.005891639 -0.005208134  0.009309790 0.0058092898  0.0142507410
##              Fst11       Fst12        Fst13        Fst14
## Lag 0   1.00000000 1.000000000  1.000000000  1.000000000
## Lag 10  0.19589327 0.095557353  0.068671879  0.175198841
## Lag 50  0.01160236 0.016612233  0.008869372  0.023110338
## Lag 100 0.00241594 0.021772057  0.008850768 -0.011902869
## Lag 500 0.01472459 0.007206443 -0.010452274 -0.008091698
levelplot(t(autocorr.diag(chain)))

plot(chain)

2R temp

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/1.temp.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed          1      0.512  
## Fst2  passed          1      0.131  
## Fst3  passed          1      0.106  
## Fst4  passed          1      0.200  
## Fst5  passed          1      0.611  
## Fst6  passed          1      0.747  
## Fst7  passed          1      0.436  
## Fst8  passed          1      0.528  
## Fst9  passed          1      0.440  
## Fst10 passed          1      0.724  
## Fst11 passed       1501      0.194  
## Fst12 passed       1501      0.107  
## Fst13 passed          1      0.457  
## Fst14 passed          1      0.303  
##                                 
##       Halfwidth Mean   Halfwidth
##       test                      
## Fst1  passed    0.0551 6.39e-06 
## Fst2  passed    0.0245 3.77e-06 
## Fst3  passed    0.0369 4.40e-06 
## Fst4  passed    0.0294 4.02e-06 
## Fst5  passed    0.0345 6.38e-06 
## Fst6  passed    0.0291 5.69e-06 
## Fst7  passed    0.0450 4.71e-06 
## Fst8  passed    0.0550 5.08e-06 
## Fst9  passed    0.0195 2.90e-06 
## Fst10 passed    0.0220 3.04e-06 
## Fst11 passed    0.0576 7.04e-06 
## Fst12 passed    0.4600 3.02e-05 
## Fst13 passed    0.1145 1.07e-05 
## Fst14 passed    0.0290 3.62e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 3147.592 3032.482 3637.943 3520.779 3855.624 3867.232 3878.774 3543.014 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 3047.562 3171.897 3156.707 4320.713 4009.916 3657.050
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                 Fst1         Fst2         Fst3         Fst4        Fst5
## Lag 0    1.000000000  1.000000000  1.000000000  1.000000000  1.00000000
## Lag 10   0.162016597  0.188756270  0.205311851  0.173407605  0.12902914
## Lag 50   0.011317061  0.032577714  0.007260828  0.021289417  0.01974620
## Lag 100 -0.013950648  0.006557931 -0.029392754  0.029723675 -0.01064528
## Lag 500 -0.009077435 -0.001422646 -0.009115234 -0.004990869 -0.02831390
##                Fst6        Fst7          Fst8         Fst9        Fst10
## Lag 0    1.00000000  1.00000000  1.000000e+00  1.000000000  1.000000000
## Lag 10   0.12755090  0.12608475  1.467368e-01  0.212852113  0.223516040
## Lag 50   0.01034468 -0.01252907 -1.190971e-02  0.007647009  0.024294209
## Lag 100 -0.01039281 -0.00161087  2.179456e-02 -0.001098868 -0.002648697
## Lag 500 -0.01365207  0.03881780  6.817774e-05  0.005740903  0.012263952
##                Fst11        Fst12         Fst13        Fst14
## Lag 0    1.000000000  1.000000000  1.0000000000  1.000000000
## Lag 10   0.225795035  0.093451554  0.0794489309  0.108732888
## Lag 50  -0.014756946  0.003534171 -0.0007704787 -0.001769024
## Lag 100  0.021502898 -0.022624588  0.0360885518 -0.008080128
## Lag 500  0.001925811 -0.000765012  0.0070347195  0.023141724
levelplot(t(autocorr.diag(chain)))

plot(chain)

2L temp

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/2.temp.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed       1         0.252  
## Fst2  passed       1         0.483  
## Fst3  passed       1         0.695  
## Fst4  passed       1         0.561  
## Fst5  passed       1         0.589  
## Fst6  passed       1         0.599  
## Fst7  passed       1         0.914  
## Fst8  passed       1         0.413  
## Fst9  passed       1         0.203  
## Fst10 passed       1         0.793  
## Fst11 passed       1         0.614  
## Fst12 passed       1         0.139  
## Fst13 passed       1         0.749  
## Fst14 passed       1         0.186  
##                                 
##       Halfwidth Mean   Halfwidth
##       test                      
## Fst1  passed    0.0987 1.28e-05 
## Fst2  passed    0.0803 1.09e-05 
## Fst3  passed    0.0807 1.11e-05 
## Fst4  passed    0.0360 5.89e-06 
## Fst5  passed    0.0263 7.29e-06 
## Fst6  passed    0.0323 7.18e-06 
## Fst7  passed    0.0355 5.44e-06 
## Fst8  passed    0.0548 6.35e-06 
## Fst9  passed    0.0132 3.26e-06 
## Fst10 passed    0.0400 6.46e-06 
## Fst11 passed    0.0655 8.03e-06 
## Fst12 passed    0.4349 3.12e-05 
## Fst13 passed    0.1597 1.83e-05 
## Fst14 passed    0.0450 6.66e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 3004.487 3079.618 2825.089 3353.079 3308.912 3842.727 3206.397 3563.200 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 2445.859 2527.364 2987.745 4255.227 3112.026 3019.459
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                Fst1         Fst2         Fst3        Fst4         Fst5
## Lag 0   1.000000000  1.000000000  1.000000000  1.00000000  1.000000000
## Lag 10  0.198233042  0.218869974  0.234386224  0.19697110  0.172292592
## Lag 50  0.020841610  0.006814003  0.003999675 -0.02417961 -0.006923424
## Lag 100 0.003177167  0.010716036 -0.021364061  0.02086044  0.011375946
## Lag 500 0.009543020 -0.003226655  0.007262111 -0.01339249  0.012680167
##                  Fst6        Fst7         Fst8        Fst9        Fst10
## Lag 0    1.0000000000 1.000000000  1.000000000 1.000000000  1.000000000
## Lag 10   0.1306762582 0.186542593  0.167593258 0.302500201  0.248775291
## Lag 50  -0.0187893519 0.009638212  0.009734679 0.011304649  0.034681577
## Lag 100 -0.0152534507 0.022995285 -0.004214589 0.007197374 -0.019834954
## Lag 500  0.0002226343 0.015299374  0.025396423 0.011665819 -0.006464729
##               Fst11         Fst12        Fst13         Fst14
## Lag 0   1.000000000  1.0000000000  1.000000000  1.0000000000
## Lag 10  0.194434976  0.0802717949  0.163286069  0.1810286584
## Lag 50  0.007495038  0.0060742824  0.032511141  0.0115024485
## Lag 100 0.024054627 -0.0007071935 -0.031589714 -0.0009281255
## Lag 500 0.000458376 -0.0056029174  0.009730656 -0.0095717329
levelplot(t(autocorr.diag(chain)))

plot(chain)

3R temp

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/3.temp.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed          1      0.7912 
## Fst2  passed          1      0.0926 
## Fst3  passed          1      0.2605 
## Fst4  passed          1      0.2424 
## Fst5  passed          1      0.0605 
## Fst6  passed          1      0.2854 
## Fst7  passed          1      0.5350 
## Fst8  passed          1      0.5128 
## Fst9  passed          1      0.4396 
## Fst10 passed          1      0.4969 
## Fst11 passed       1001      0.0512 
## Fst12 passed          1      0.6047 
## Fst13 passed          1      0.6322 
## Fst14 passed          1      0.2153 
##                                  
##       Halfwidth Mean    Halfwidth
##       test                       
## Fst1  passed    0.01747 3.29e-06 
## Fst2  passed    0.00548 1.62e-06 
## Fst3  passed    0.00911 2.24e-06 
## Fst4  passed    0.01690 2.86e-06 
## Fst5  passed    0.02159 5.11e-06 
## Fst6  passed    0.02049 4.98e-06 
## Fst7  passed    0.01927 3.01e-06 
## Fst8  passed    0.05708 5.27e-06 
## Fst9  passed    0.00964 2.00e-06 
## Fst10 passed    0.01025 1.79e-06 
## Fst11 passed    0.03520 4.59e-06 
## Fst12 passed    0.26936 2.27e-05 
## Fst13 passed    0.08160 8.84e-06 
## Fst14 passed    0.01922 2.99e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 3072.853 3741.941 3030.960 3934.210 4139.249 4007.662 3551.750 4009.870 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 2843.208 3390.236 4101.686 4277.894 3859.239 3492.174
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                 Fst1        Fst2        Fst3        Fst4          Fst5
## Lag 0    1.000000000  1.00000000  1.00000000 1.000000000  1.0000000000
## Lag 10   0.195851314  0.16682696  0.21619896 0.119095949  0.0939835255
## Lag 50   0.004531160  0.02180416  0.01377274 0.002039917 -0.0062985118
## Lag 100  0.011454137 -0.04237385 -0.05194721 0.001826051  0.0002917281
## Lag 500 -0.003774316 -0.01411793 -0.01010821 0.001678955  0.0026022636
##                  Fst6        Fst7          Fst8          Fst9       Fst10
## Lag 0    1.0000000000 1.000000000  1.0000000000  1.0000000000  1.00000000
## Lag 10   0.0681918015 0.146083699  0.1096962846  0.1976099152  0.19502127
## Lag 50   0.0001229178 0.010113647 -0.0001008117  0.0124014381 -0.01164943
## Lag 100 -0.0198575072 0.002378411  0.0021872289 -0.0001017405 -0.02146946
## Lag 500 -0.0041192153 0.002940570 -0.0013853196 -0.0063093198  0.01047925
##                Fst11        Fst12         Fst13       Fst14
## Lag 0   1.0000000000  1.000000000  1.0000000000 1.000000000
## Lag 10  0.1451900116  0.077631948  0.0878510318 0.142727856
## Lag 50  0.0003551114  0.003535068 -0.0122409057 0.002405534
## Lag 100 0.0060063908  0.013785207  0.0005460158 0.006498615
## Lag 500 0.0036933326 -0.002256197  0.0052509239 0.003796013
levelplot(t(autocorr.diag(chain)))

plot(chain)

3L temp

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/4.temp.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed       1         0.1247 
## Fst2  passed       1         0.1992 
## Fst3  passed       1         0.7122 
## Fst4  passed       1         0.6000 
## Fst5  passed       1         0.0811 
## Fst6  passed       1         0.8510 
## Fst7  passed       1         0.3267 
## Fst8  passed       1         0.8530 
## Fst9  passed       1         0.9213 
## Fst10 passed       1         0.5542 
## Fst11 passed       1         0.9109 
## Fst12 passed       1         0.7381 
## Fst13 passed       1         0.2924 
## Fst14 passed       1         0.3904 
##                                  
##       Halfwidth Mean    Halfwidth
##       test                       
## Fst1  passed    0.01959 3.72e-06 
## Fst2  passed    0.00621 2.16e-06 
## Fst3  passed    0.01071 2.66e-06 
## Fst4  passed    0.01299 2.79e-06 
## Fst5  passed    0.01445 5.09e-06 
## Fst6  passed    0.01364 5.00e-06 
## Fst7  passed    0.01936 3.55e-06 
## Fst8  passed    0.05308 5.76e-06 
## Fst9  passed    0.00743 1.88e-06 
## Fst10 passed    0.00747 2.01e-06 
## Fst11 passed    0.03452 4.47e-06 
## Fst12 passed    0.25525 2.55e-05 
## Fst13 passed    0.08569 1.03e-05 
## Fst14 passed    0.01828 3.11e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 3667.123 3310.481 3291.176 4194.588 4288.404 4141.828 3404.164 4147.302 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 3159.629 2687.834 3734.275 4308.080 4060.241 4076.622
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                Fst1        Fst2        Fst3         Fst4        Fst5
## Lag 0   1.000000000  1.00000000  1.00000000  1.000000000 1.000000000
## Lag 10  0.153590112  0.16092346  0.18115400  0.135210256 0.055558992
## Lag 50  0.010503836  0.01261251 -0.02023669 -0.029269439 0.003938625
## Lag 100 0.012514724 -0.02219323 -0.02360201  0.001898819 0.008062668
## Lag 500 0.006570454 -0.01706376 -0.03475784 -0.006194198 0.009411560
##                 Fst6         Fst7         Fst8         Fst9        Fst10
## Lag 0    1.000000000  1.000000000  1.000000000  1.000000000  1.000000000
## Lag 10   0.067926758  0.163776175  0.108767979  0.184321721  0.219407008
## Lag 50   0.012069505  0.008022964  0.007227502  0.003890879  0.023527835
## Lag 100 -0.005448105 -0.009787215 -0.017150126  0.001847150 -0.014093389
## Lag 500 -0.027855815 -0.003193722 -0.022414166 -0.023454967  0.003191097
##                 Fst11       Fst12         Fst13        Fst14
## Lag 0    1.0000000000 1.000000000  1.0000000000  1.000000000
## Lag 10   0.1447188161 0.074136460  0.1060089243  0.121872649
## Lag 50  -0.0008976264 0.009439436  0.0193305753  0.009724819
## Lag 100  0.0103737949 0.012650380  0.0004001628 -0.016618758
## Lag 500  0.0135648573 0.006332512 -0.0160715556 -0.014444973
levelplot(t(autocorr.diag(chain)))

plot(chain)

X temp

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/5.temp.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed       1         0.4216 
## Fst2  passed       1         0.7876 
## Fst3  passed       1         0.3875 
## Fst4  passed       1         0.1636 
## Fst5  passed       1         0.1673 
## Fst6  passed       1         0.1126 
## Fst7  passed       1         0.0639 
## Fst8  passed       1         0.3982 
## Fst9  passed       1         0.8223 
## Fst10 passed       1         0.0533 
## Fst11 passed       1         0.1277 
## Fst12 passed       1         0.6423 
## Fst13 passed       1         0.7304 
## Fst14 passed       1         0.0731 
##                                 
##       Halfwidth Mean   Halfwidth
##       test                      
## Fst1  passed    0.0275 8.69e-06 
## Fst2  passed    0.0114 4.39e-06 
## Fst3  passed    0.0155 5.72e-06 
## Fst4  passed    0.0129 4.44e-06 
## Fst5  passed    0.0207 7.64e-06 
## Fst6  passed    0.0234 8.97e-06 
## Fst7  passed    0.0124 3.96e-06 
## Fst8  passed    0.0644 9.84e-06 
## Fst9  passed    0.0125 3.41e-06 
## Fst10 passed    0.0118 3.56e-06 
## Fst11 passed    0.0361 8.31e-06 
## Fst12 passed    0.3864 4.58e-05 
## Fst13 passed    0.2267 3.11e-05 
## Fst14 passed    0.0487 9.03e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 2147.770 2594.108 2067.092 3197.879 4294.216 3981.507 3550.924 3854.050 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 3484.336 2882.584 2643.258 4073.167 4078.991 3757.505
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                 Fst1         Fst2       Fst3         Fst4         Fst5
## Lag 0    1.000000000  1.000000000 1.00000000  1.000000000  1.000000000
## Lag 10   0.260223983  0.230402803 0.28572674  0.194043339  0.075739116
## Lag 50   0.052569252  0.017208106 0.05605234 -0.009588056 -0.001224715
## Lag 100 -0.004981639 -0.003575993 0.02212055 -0.015511812  0.001860171
## Lag 500 -0.013828245 -0.020911686 0.01759601 -0.017340618 -0.021276632
##                  Fst6         Fst7         Fst8         Fst9       Fst10
## Lag 0    1.0000000000  1.000000000  1.000000000  1.000000000 1.000000000
## Lag 10   0.0878072635  0.135684714  0.108457071  0.178448875 0.219619551
## Lag 50  -0.0119546241 -0.006136479  0.001609714  0.012264797 0.019802088
## Lag 100  0.0097256926  0.023638165 -0.007415945  0.008854932 0.019480080
## Lag 500 -0.0005856327 -0.039505605  0.007141300 -0.035367941 0.004210166
##               Fst11        Fst12        Fst13        Fst14
## Lag 0   1.000000000  1.000000000  1.000000000  1.000000000
## Lag 10  0.227571101  0.101953016  0.101245988  0.141681650
## Lag 50  0.036457396 -0.023275085  0.013665207 -0.001819869
## Lag 100 0.003876193 -0.003250872  0.007178394  0.014500964
## Lag 500 0.016300111  0.010585012 -0.013174184  0.010089182
levelplot(t(autocorr.diag(chain)))

plot(chain)

2R precip

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/1.prec.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed          1      0.1489 
## Fst2  passed          1      0.0789 
## Fst3  failed         NA      0.0065 
## Fst4  passed          1      0.6411 
## Fst5  passed          1      0.6804 
## Fst6  passed          1      0.3438 
## Fst7  passed          1      0.5229 
## Fst8  passed          1      0.2314 
## Fst9  passed       1001      0.0609 
## Fst10 passed          1      0.5280 
## Fst11 passed          1      0.1765 
## Fst12 passed          1      0.1139 
## Fst13 passed          1      0.7013 
## Fst14 passed          1      0.5901 
##                                 
##       Halfwidth Mean   Halfwidth
##       test                      
## Fst1  passed    0.0596 7.07e-06 
## Fst2  passed    0.0251 4.02e-06 
## Fst3  <NA>          NA       NA 
## Fst4  passed    0.0309 3.94e-06 
## Fst5  passed    0.0340 6.07e-06 
## Fst6  passed    0.0292 5.05e-06 
## Fst7  passed    0.0423 4.70e-06 
## Fst8  passed    0.0561 5.11e-06 
## Fst9  passed    0.0203 3.35e-06 
## Fst10 passed    0.0232 3.33e-06 
## Fst11 passed    0.0616 5.68e-06 
## Fst12 passed    0.4528 2.78e-05 
## Fst13 passed    0.1111 1.06e-05 
## Fst14 passed    0.0250 3.36e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 2900.167 2782.632 2880.959 3841.719 3925.197 4431.342 3502.439 3625.023 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 3006.681 2868.145 3490.787 3903.277 3922.602 3813.897
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                 Fst1        Fst2          Fst3         Fst4         Fst5
## Lag 0    1.000000000 1.000000000  1.0000000000  1.000000000  1.000000000
## Lag 10   0.186508727 0.222774719  0.2283277938  0.130805132  0.088827207
## Lag 50   0.026588679 0.014936599  0.0271407235  0.007925273 -0.023090505
## Lag 100 -0.003082062 0.004046256 -0.0007477726 -0.014120710 -0.005278209
## Lag 500  0.009450721 0.009399297 -0.0106981526  0.003533678 -0.014557523
##                 Fst6         Fst7        Fst8        Fst9         Fst10
## Lag 0    1.000000000  1.000000000 1.000000000 1.000000000  1.0000000000
## Lag 10   0.060095117  0.147280952 0.159222157 0.209578475  0.2440846503
## Lag 50   0.014072529 -0.023679778 0.004837739 0.004288884 -0.0005706357
## Lag 100 -0.002659503  0.001112557 0.012911794 0.008608064 -0.0288967689
## Lag 500  0.006154117  0.035265339 0.024450824 0.006690856  0.0055199553
##                Fst11        Fst12        Fst13        Fst14
## Lag 0    1.000000000  1.000000000 1.0000000000  1.000000000
## Lag 10   0.157926393  0.099294904 0.1205521981  0.134375439
## Lag 50   0.009547921  0.007786595 0.0038005587  0.004498597
## Lag 100  0.012993270 -0.004707591 0.0087332726 -0.002359100
## Lag 500 -0.008541712 -0.008532294 0.0004562545 -0.008417248
levelplot(t(autocorr.diag(chain)))

plot(chain)

2L precip

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/2.prec.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed       1         0.3309 
## Fst2  passed       1         0.9388 
## Fst3  passed       1         0.6882 
## Fst4  passed       1         0.5698 
## Fst5  passed       1         0.3761 
## Fst6  passed       1         0.0578 
## Fst7  passed       1         0.8605 
## Fst8  passed       1         0.5517 
## Fst9  passed       1         0.2227 
## Fst10 passed       1         0.5938 
## Fst11 passed       1         0.6640 
## Fst12 passed       1         0.7841 
## Fst13 passed       1         0.2328 
## Fst14 passed       1         0.2760 
##                                 
##       Halfwidth Mean   Halfwidth
##       test                      
## Fst1  passed    0.1190 1.38e-05 
## Fst2  passed    0.0961 1.12e-05 
## Fst3  passed    0.0951 1.12e-05 
## Fst4  passed    0.0391 6.07e-06 
## Fst5  passed    0.0244 6.53e-06 
## Fst6  passed    0.0294 7.04e-06 
## Fst7  passed    0.0330 5.19e-06 
## Fst8  passed    0.0574 6.35e-06 
## Fst9  passed    0.0145 3.53e-06 
## Fst10 passed    0.0454 6.53e-06 
## Fst11 passed    0.0785 8.32e-06 
## Fst12 passed    0.4332 3.28e-05 
## Fst13 passed    0.1534 1.68e-05 
## Fst14 passed    0.0372 5.94e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 3284.625 3287.983 3237.938 3397.167 4013.007 3552.448 3181.907 3729.855 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 2345.829 2980.152 3475.078 4101.334 3639.252 3152.176
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                  Fst1         Fst2         Fst3         Fst4        Fst5
## Lag 0    1.000000e+00  1.000000000  1.000000000  1.000000000  1.00000000
## Lag 10   1.766333e-01  0.180277678  0.170516397  0.170792038  0.10930993
## Lag 50  -1.036846e-03  0.001269064 -0.011910251 -0.031010238 -0.00847705
## Lag 100 -5.595001e-06 -0.016115596 -0.015585339  0.034103682  0.01643825
## Lag 500 -1.120024e-02 -0.016526486  0.005888039 -0.006269067 -0.01967393
##                  Fst6         Fst7         Fst8         Fst9        Fst10
## Lag 0    1.0000000000  1.000000000  1.000000000  1.000000000  1.000000000
## Lag 10   0.1239504475  0.222018705  0.120381406  0.280358795  0.205099531
## Lag 50  -0.0007255508 -0.007621358  0.010752478  0.002793641 -0.005483769
## Lag 100 -0.0135694859 -0.004082289 -0.002321631  0.001769047  0.002692973
## Lag 500  0.0147137599 -0.004804574 -0.023755424 -0.003357714 -0.015966165
##               Fst11        Fst12        Fst13       Fst14
## Lag 0    1.00000000  1.000000000  1.000000000 1.000000000
## Lag 10   0.14476195  0.098541927  0.157312454 0.226476621
## Lag 50   0.01749906  0.001334517 -0.015407404 0.008753364
## Lag 100 -0.01605734 -0.010869439 -0.006536633 0.026573469
## Lag 500 -0.01355492  0.017142958 -0.018289998 0.009261979
levelplot(t(autocorr.diag(chain)))

plot(chain)

3R precip

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/3.prec.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed       1         0.836  
## Fst2  passed       1         0.728  
## Fst3  passed       1         0.518  
## Fst4  passed       1         0.961  
## Fst5  passed       1         0.602  
## Fst6  passed       1         0.255  
## Fst7  passed       1         0.500  
## Fst8  passed       1         0.238  
## Fst9  passed       1         0.453  
## Fst10 passed       1         0.656  
## Fst11 passed       1         0.866  
## Fst12 passed       1         0.643  
## Fst13 passed       1         0.918  
## Fst14 passed       1         0.173  
##                                  
##       Halfwidth Mean    Halfwidth
##       test                       
## Fst1  passed    0.02006 3.66e-06 
## Fst2  passed    0.00593 2.13e-06 
## Fst3  passed    0.00997 2.39e-06 
## Fst4  passed    0.01888 3.22e-06 
## Fst5  passed    0.02142 5.01e-06 
## Fst6  passed    0.02034 4.46e-06 
## Fst7  passed    0.01745 2.82e-06 
## Fst8  passed    0.05851 5.73e-06 
## Fst9  passed    0.01077 1.96e-06 
## Fst10 passed    0.01167 2.22e-06 
## Fst11 passed    0.03921 4.11e-06 
## Fst12 passed    0.26340 2.28e-05 
## Fst13 passed    0.07949 8.65e-06 
## Fst14 passed    0.01668 2.82e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 3093.838 2693.221 2891.973 3287.499 4226.414 4672.749 3476.363 3749.288 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 3352.838 2777.592 3667.672 4178.734 3957.035 3602.131
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                 Fst1        Fst2          Fst3        Fst4        Fst5
## Lag 0    1.000000000  1.00000000  1.0000000000 1.000000000  1.00000000
## Lag 10   0.185221876  0.22442959  0.2183894331 0.153978352  0.08364600
## Lag 50   0.024299614  0.01200389  0.0376251446 0.001627516  0.02749681
## Lag 100 -0.013169613 -0.02520475 -0.0193488627 0.003176575 -0.01430959
## Lag 500 -0.002341593 -0.01137294 -0.0006074088 0.032586827 -0.01631854
##                  Fst6          Fst7       Fst8         Fst9        Fst10
## Lag 0    1.0000000000  1.0000000000 1.00000000  1.000000000  1.000000000
## Lag 10   0.0573412424  0.1532439733 0.11592008  0.182017089  0.226338693
## Lag 50  -0.0329665720  0.0056625750 0.01900974 -0.001877686  0.035900900
## Lag 100 -0.0107043120 -0.0028384123 0.02302985 -0.014832551  0.026540401
## Lag 500 -0.0003529382  0.0004135391 0.00509871  0.015635906 -0.007443639
##                Fst11        Fst12       Fst13       Fst14
## Lag 0    1.000000000  1.000000000 1.000000000 1.000000000
## Lag 10   0.153517005  0.089276405 0.076428766 0.162307889
## Lag 50   0.014180211 -0.011086618 0.020448143 0.015135153
## Lag 100 -0.021009544 -0.004781642 0.016415723 0.003967045
## Lag 500 -0.006890984 -0.015677964 0.009459596 0.007700519
levelplot(t(autocorr.diag(chain)))

plot(chain)

3L precip

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/4.prec.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed         1       0.171  
## Fst2  passed         1       0.181  
## Fst3  passed         1       0.186  
## Fst4  passed         1       0.494  
## Fst5  passed       501       0.200  
## Fst6  passed         1       0.458  
## Fst7  passed         1       0.315  
## Fst8  passed         1       0.541  
## Fst9  passed         1       0.171  
## Fst10 passed         1       0.143  
## Fst11 passed         1       0.119  
## Fst12 passed         1       0.816  
## Fst13 passed         1       0.450  
## Fst14 passed         1       0.736  
##                                  
##       Halfwidth Mean    Halfwidth
##       test                       
## Fst1  passed    0.02207 4.26e-06 
## Fst2  passed    0.00685 2.63e-06 
## Fst3  passed    0.01177 2.98e-06 
## Fst4  passed    0.01413 3.06e-06 
## Fst5  passed    0.01425 5.09e-06 
## Fst6  passed    0.01354 4.84e-06 
## Fst7  passed    0.01780 3.21e-06 
## Fst8  passed    0.05416 5.74e-06 
## Fst9  passed    0.00802 2.08e-06 
## Fst10 passed    0.00816 1.97e-06 
## Fst11 passed    0.03743 4.79e-06 
## Fst12 passed    0.25212 2.53e-05 
## Fst13 passed    0.08408 9.87e-06 
## Fst14 passed    0.01641 3.19e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 3355.997 2509.113 2914.500 3606.196 4556.047 4045.471 3687.627 4088.689 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 3060.314 3166.897 3539.284 4469.910 4339.374 3735.076
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                Fst1        Fst2         Fst3          Fst4         Fst5
## Lag 0    1.00000000  1.00000000  1.000000000  1.0000000000  1.000000000
## Lag 10   0.15616725  0.20657693  0.209479211  0.1013654638  0.046258126
## Lag 50   0.01829202  0.03286747  0.031088089  0.0027221068  0.002747186
## Lag 100  0.02682659 -0.02756767 -0.001650494 -0.0008054601 -0.009710605
## Lag 500 -0.02291298 -0.00260386 -0.001794493  0.0154879287  0.009576140
##                Fst6         Fst7         Fst8          Fst9        Fst10
## Lag 0    1.00000000  1.000000000  1.000000000  1.0000000000  1.000000000
## Lag 10   0.05673575  0.150866866  0.114406604  0.2104475237  0.186960009
## Lag 50   0.01508705 -0.018845235 -0.006579446 -0.0065141284  0.006879469
## Lag 100 -0.02424429  0.008579265  0.034956354 -0.0001781693 -0.011529425
## Lag 500  0.00757516  0.028418991 -0.003449054 -0.0088582346 -0.005266466
##                Fst11        Fst12       Fst13        Fst14
## Lag 0    1.000000000  1.000000000  1.00000000  1.000000000
## Lag 10   0.143429990  0.055776790  0.07053657  0.144613913
## Lag 50   0.009178059 -0.011219973  0.01051711  0.004026944
## Lag 100  0.009185220 -0.020327791 -0.01951898  0.021894423
## Lag 500 -0.021234655 -0.001247939 -0.03054100 -0.006748757
levelplot(t(autocorr.diag(chain)))

plot(chain)

X precip

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/5.prec.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed       1         0.8692 
## Fst2  passed       1         0.2105 
## Fst3  passed       1         0.6779 
## Fst4  passed       1         0.0763 
## Fst5  passed       1         0.9235 
## Fst6  passed       1         0.3293 
## Fst7  passed       1         0.6249 
## Fst8  passed       1         0.6401 
## Fst9  passed       1         0.3392 
## Fst10 passed       1         0.4518 
## Fst11 passed       1         0.5024 
## Fst12 passed       1         0.3065 
## Fst13 passed       1         0.3790 
## Fst14 passed       1         0.3315 
##                                  
##       Halfwidth Mean    Halfwidth
##       test                       
## Fst1  passed    0.03848 1.01e-05 
## Fst2  passed    0.01593 5.78e-06 
## Fst3  passed    0.02137 6.69e-06 
## Fst4  passed    0.01918 4.90e-06 
## Fst5  passed    0.02252 8.13e-06 
## Fst6  passed    0.02327 7.91e-06 
## Fst7  passed    0.00959 3.44e-06 
## Fst8  passed    0.06953 1.07e-05 
## Fst9  passed    0.01684 4.20e-06 
## Fst10 passed    0.01691 4.19e-06 
## Fst11 passed    0.04885 9.54e-06 
## Fst12 passed    0.34245 4.65e-05 
## Fst13 passed    0.22117 3.16e-05 
## Fst14 passed    0.03725 8.08e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 2531.963 2342.168 2324.951 3975.034 3849.335 4092.738 3566.071 3670.116 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 3593.416 3477.464 2955.756 4052.768 3879.510 3516.342
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                 Fst1        Fst2         Fst3         Fst4         Fst5
## Lag 0    1.000000000 1.000000000  1.000000000  1.000000000  1.000000000
## Lag 10   0.246548139 0.256448354  0.255991229  0.114004436  0.069603239
## Lag 50   0.035495356 0.013919229  0.041415868  0.017178758  0.007204155
## Lag 100 -0.003505592 0.005144498 -0.000388012 -0.007104983 -0.007348997
## Lag 500 -0.026938095 0.007829937 -0.004074389  0.002623357 -0.016379442
##                 Fst6         Fst7         Fst8        Fst9        Fst10
## Lag 0    1.000000000  1.000000000  1.000000000  1.00000000  1.000000000
## Lag 10   0.089631359  0.167201936  0.114054598  0.16348689  0.179404505
## Lag 50  -0.004999225 -0.012545387  0.002990064  0.01850357  0.011309686
## Lag 100  0.022416478  0.022707686 -0.009676859  0.02520612 -0.004892306
## Lag 500  0.009792738  0.002025453  0.007302044 -0.03417012  0.016233532
##                Fst11        Fst12        Fst13        Fst14
## Lag 0    1.000000000  1.000000000  1.000000000  1.000000000
## Lag 10   0.197376784  0.104436694  0.089460856  0.174019015
## Lag 50   0.007540276 -0.004457085  0.023076191 -0.004640746
## Lag 100 -0.001933773  0.017538614  0.002879939  0.002403782
## Lag 500 -0.025780900  0.016134327 -0.013668942  0.013403557
levelplot(t(autocorr.diag(chain)))

plot(chain)

2R rand

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/1.rand.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed          1      0.6574 
## Fst2  passed          1      0.9453 
## Fst3  passed          1      0.3110 
## Fst4  passed          1      0.6462 
## Fst5  passed          1      0.7178 
## Fst6  passed          1      0.5493 
## Fst7  passed          1      0.5725 
## Fst8  passed          1      0.6989 
## Fst9  passed          1      0.0984 
## Fst10 passed          1      0.0605 
## Fst11 passed          1      0.1486 
## Fst12 passed          1      0.3158 
## Fst13 passed          1      0.4384 
## Fst14 passed       1001      0.0640 
##                                 
##       Halfwidth Mean   Halfwidth
##       test                      
## Fst1  passed    0.0581 6.69e-06 
## Fst2  passed    0.0238 3.55e-06 
## Fst3  passed    0.0378 4.92e-06 
## Fst4  passed    0.0290 3.92e-06 
## Fst5  passed    0.0320 5.72e-06 
## Fst6  passed    0.0303 5.68e-06 
## Fst7  passed    0.0423 4.58e-06 
## Fst8  passed    0.0555 5.16e-06 
## Fst9  passed    0.0192 2.95e-06 
## Fst10 passed    0.0234 3.35e-06 
## Fst11 passed    0.0576 6.05e-06 
## Fst12 passed    0.4527 2.65e-05 
## Fst13 passed    0.1120 1.03e-05 
## Fst14 passed    0.0280 3.82e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 3188.282 3389.091 3129.626 3651.796 4275.484 4031.801 3746.300 3480.751 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 2925.427 2941.609 2929.625 4119.397 4166.192 3883.760
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                 Fst1         Fst2         Fst3        Fst4        Fst5
## Lag 0    1.000000000  1.000000000  1.000000000  1.00000000  1.00000000
## Lag 10   0.184714663  0.191831556  0.210721507  0.15732321  0.07791202
## Lag 50   0.027215369  0.012065056  0.026753678 -0.01808314 -0.02350385
## Lag 100  0.002306636  0.005907411 -0.022273182  0.01664383  0.01283224
## Lag 500 -0.015786914 -0.015651730  0.001208656 -0.01252487  0.01970700
##                 Fst6         Fst7         Fst8         Fst9        Fst10
## Lag 0    1.000000000  1.000000000  1.000000000  1.000000000  1.000000000
## Lag 10   0.072927723  0.143144626  0.178947255  0.225028469  0.235612407
## Lag 50  -0.011136590 -0.024166320 -0.022281594  0.008758923  0.008084121
## Lag 100 -0.008340693 -0.012098104 -0.004644657 -0.011532431 -0.012365571
## Lag 500 -0.009933778 -0.007334487 -0.003486653 -0.004045488  0.004411528
##                Fst11        Fst12       Fst13        Fst14
## Lag 0    1.000000000  1.000000000  1.00000000 1.0000000000
## Lag 10   0.220242041  0.096365478  0.09076723 0.1254525507
## Lag 50   0.023864054 -0.002768341 -0.02019092 0.0281204942
## Lag 100 -0.035434942 -0.013167522  0.01247540 0.0066609214
## Lag 500 -0.002498027  0.011559640 -0.02193530 0.0005111049
levelplot(t(autocorr.diag(chain)))

plot(chain)

2L rand

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/2.rand.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed       1         0.902  
## Fst2  passed       1         0.945  
## Fst3  passed       1         0.875  
## Fst4  passed       1         0.732  
## Fst5  passed       1         0.365  
## Fst6  passed       1         0.432  
## Fst7  passed       1         0.694  
## Fst8  passed       1         0.905  
## Fst9  passed       1         0.948  
## Fst10 passed       1         0.843  
## Fst11 passed       1         0.361  
## Fst12 passed       1         0.274  
## Fst13 passed       1         0.860  
## Fst14 passed       1         0.232  
##                                 
##       Halfwidth Mean   Halfwidth
##       test                      
## Fst1  passed    0.1175 1.32e-05 
## Fst2  passed    0.0865 1.19e-05 
## Fst3  passed    0.0918 1.08e-05 
## Fst4  passed    0.0354 6.10e-06 
## Fst5  passed    0.0211 6.63e-06 
## Fst6  passed    0.0328 8.12e-06 
## Fst7  passed    0.0325 5.17e-06 
## Fst8  passed    0.0565 6.21e-06 
## Fst9  passed    0.0136 3.52e-06 
## Fst10 passed    0.0480 6.89e-06 
## Fst11 passed    0.0711 8.72e-06 
## Fst12 passed    0.4272 3.15e-05 
## Fst13 passed    0.1513 1.75e-05 
## Fst14 passed    0.0406 6.17e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 3257.721 2959.814 3364.339 3057.826 3514.396 3333.265 3199.952 3856.727 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 2217.834 3038.246 2922.385 4287.008 3175.443 3123.072
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                Fst1        Fst2         Fst3         Fst4         Fst5
## Lag 0   1.000000000 1.000000000  1.000000000  1.000000000  1.000000000
## Lag 10  0.173916955 0.218377369  0.178808977  0.221643577  0.145702968
## Lag 50  0.016586936 0.009386048  0.006737755  0.008179841 -0.011383141
## Lag 100 0.010418862 0.017910051 -0.015865436 -0.012018199 -0.008855598
## Lag 500 0.001915428 0.014655187 -0.005591513  0.007316846  0.019453875
##                 Fst6         Fst7         Fst8         Fst9        Fst10
## Lag 0    1.000000000  1.000000000  1.000000000  1.000000000  1.000000000
## Lag 10   0.139811822  0.161925573  0.128888476  0.312080447  0.209405994
## Lag 50   0.012755358  0.006319802  0.004649574  0.028790475  0.021844110
## Lag 100 -0.009783806 -0.015589399 -0.014265685 -0.007784115 -0.020723768
## Lag 500 -0.009735445  0.020159596  0.016054284 -0.013096760  0.003153665
##                Fst11        Fst12        Fst13        Fst14
## Lag 0    1.000000000  1.000000000  1.000000000  1.000000000
## Lag 10   0.224085616  0.076574181  0.164035323  0.187328545
## Lag 50   0.023776450  0.011721616  0.002945322 -0.008587925
## Lag 100 -0.012021123 -0.005837795 -0.002331670 -0.006443290
## Lag 500  0.007844993 -0.001537194 -0.023219704 -0.010031280
levelplot(t(autocorr.diag(chain)))

#plot(chain)
#chain checked visually not shown here to save file size

3R rand

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/3.rand.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed       1         0.698  
## Fst2  passed       1         0.444  
## Fst3  passed       1         0.264  
## Fst4  passed       1         0.634  
## Fst5  passed       1         0.753  
## Fst6  passed       1         0.761  
## Fst7  passed       1         0.889  
## Fst8  passed       1         0.858  
## Fst9  passed       1         0.939  
## Fst10 passed       1         0.848  
## Fst11 passed       1         0.505  
## Fst12 passed       1         0.787  
## Fst13 passed       1         0.758  
## Fst14 passed       1         0.251  
##                                  
##       Halfwidth Mean    Halfwidth
##       test                       
## Fst1  passed    0.02009 3.93e-06 
## Fst2  passed    0.00511 1.85e-06 
## Fst3  passed    0.00993 2.44e-06 
## Fst4  passed    0.01689 3.03e-06 
## Fst5  passed    0.01899 5.10e-06 
## Fst6  passed    0.02124 5.20e-06 
## Fst7  passed    0.01692 2.71e-06 
## Fst8  passed    0.05783 5.50e-06 
## Fst9  passed    0.00991 2.22e-06 
## Fst10 passed    0.01206 2.36e-06 
## Fst11 passed    0.03574 4.24e-06 
## Fst12 passed    0.25975 2.29e-05 
## Fst13 passed    0.07960 8.35e-06 
## Fst14 passed    0.01817 2.73e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 2525.655 3217.245 2661.084 3501.064 3802.210 3722.122 3516.279 3877.517 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 2483.876 2767.304 3302.869 4117.010 4147.596 3786.155
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                 Fst1         Fst2       Fst3        Fst4        Fst5
## Lag 0    1.000000000  1.000000000 1.00000000 1.000000000  1.00000000
## Lag 10   0.176461018  0.179739994 0.18817068 0.151794806  0.08128458
## Lag 50   0.014582962  0.019316639 0.02394231 0.014730435  0.01378353
## Lag 100 -0.015411410 -0.008330382 0.01545078 0.004048946 -0.01564760
## Lag 500 -0.001237417 -0.006350326 0.01113369 0.017394197  0.01406249
##                 Fst6        Fst7         Fst8       Fst9        Fst10
## Lag 0    1.000000000 1.000000000  1.000000000 1.00000000  1.000000000
## Lag 10   0.072519887 0.139560982  0.126244267 0.21980929  0.238079647
## Lag 50   0.005671132 0.013274500 -0.006651277 0.01945987 -0.016800518
## Lag 100 -0.020089587 0.012840250  0.025278697 0.01296286  0.007643752
## Lag 500 -0.007844849 0.003094601 -0.018783885 0.01928526  0.000521164
##                 Fst11        Fst12        Fst13       Fst14
## Lag 0    1.0000000000  1.000000000  1.000000000  1.00000000
## Lag 10   0.1698166332  0.096652607  0.056543984  0.13795805
## Lag 50  -0.0114375766  0.007914173  0.004611181 -0.01411984
## Lag 100  0.0151359079 -0.013874775 -0.024511675 -0.01162457
## Lag 500 -0.0002839298 -0.009079355  0.001426539 -0.01984192
levelplot(t(autocorr.diag(chain)))

#plot(chain)
#chain checked visually not shown here to save file size

3L rand

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/4.rand.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed       1         0.0819 
## Fst2  passed       1         0.6922 
## Fst3  passed       1         0.2598 
## Fst4  passed       1         0.2878 
## Fst5  passed       1         0.3931 
## Fst6  passed       1         0.0884 
## Fst7  passed       1         0.7681 
## Fst8  passed       1         0.9699 
## Fst9  passed       1         0.2433 
## Fst10 passed       1         0.5995 
## Fst11 passed       1         0.3980 
## Fst12 passed       1         0.3205 
## Fst13 passed       1         0.7607 
## Fst14 passed       1         0.6768 
##                                  
##       Halfwidth Mean    Halfwidth
##       test                       
## Fst1  passed    0.02197 4.34e-06 
## Fst2  passed    0.00598 2.16e-06 
## Fst3  passed    0.01153 2.84e-06 
## Fst4  passed    0.01285 2.96e-06 
## Fst5  passed    0.01248 4.83e-06 
## Fst6  passed    0.01388 4.74e-06 
## Fst7  passed    0.01724 3.30e-06 
## Fst8  passed    0.05360 5.70e-06 
## Fst9  passed    0.00751 1.93e-06 
## Fst10 passed    0.00833 2.00e-06 
## Fst11 passed    0.03486 4.77e-06 
## Fst12 passed    0.24880 2.55e-05 
## Fst13 passed    0.08400 9.74e-06 
## Fst14 passed    0.01750 3.10e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 2964.556 3358.155 3102.710 3716.205 4275.984 4646.524 3211.386 4159.778 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 3143.329 3183.884 3299.012 4151.293 4359.127 3682.360
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                 Fst1         Fst2        Fst3         Fst4         Fst5
## Lag 0    1.000000000  1.000000000  1.00000000  1.000000000  1.000000000
## Lag 10   0.196676938  0.172814168  0.17278142  0.147092549  0.063514450
## Lag 50   0.015562083  0.011339276  0.01131875 -0.009498047  0.017808546
## Lag 100 -0.007951344 -0.001966161 -0.01630570 -0.007517068 -0.031511698
## Lag 500  0.002443305  0.015352459  0.03328917  0.003436301 -0.003281559
##                 Fst6        Fst7         Fst8       Fst9        Fst10
## Lag 0    1.000000000  1.00000000  1.000000000 1.00000000  1.000000000
## Lag 10   0.064519844  0.16845074  0.091531085 0.18228668  0.185856547
## Lag 50   0.002178332 -0.02166698  0.000806102 0.01051918  0.016192437
## Lag 100 -0.004012611 -0.01096253 -0.010769341 0.02633049 -0.005121142
## Lag 500 -0.019631251 -0.01539135 -0.009434356 0.01495484  0.020505785
##                Fst11       Fst12        Fst13        Fst14
## Lag 0    1.000000000 1.000000000  1.000000000  1.000000000
## Lag 10   0.163373180 0.092543436  0.068276640  0.130108662
## Lag 50   0.009666382 0.018854557 -0.008055092 -0.001487244
## Lag 100 -0.013206618 0.019240878 -0.006243558 -0.017340689
## Lag 500  0.002262949 0.005063484 -0.005790405  0.008740231
levelplot(t(autocorr.diag(chain)))

#plot(chain)
#chain checked visually not shown here to save file size

X rand

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/5.rand.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed       1         0.4130 
## Fst2  passed       1         0.7538 
## Fst3  passed       1         0.7695 
## Fst4  passed       1         0.8422 
## Fst5  passed       1         0.8118 
## Fst6  passed       1         0.9362 
## Fst7  passed       1         0.0611 
## Fst8  passed       1         0.5466 
## Fst9  passed       1         0.5979 
## Fst10 passed       1         0.6868 
## Fst11 passed       1         0.1710 
## Fst12 passed       1         0.4383 
## Fst13 passed       1         0.9111 
## Fst14 passed       1         0.4081 
##                                  
##       Halfwidth Mean    Halfwidth
##       test                       
## Fst1  passed    0.04549 1.12e-05 
## Fst2  passed    0.01036 5.68e-06 
## Fst3  passed    0.02012 6.37e-06 
## Fst4  passed    0.01262 5.79e-06 
## Fst5  passed    0.01360 8.01e-06 
## Fst6  passed    0.02573 9.82e-06 
## Fst7  passed    0.00778 3.62e-06 
## Fst8  passed    0.06754 1.02e-05 
## Fst9  passed    0.01345 4.54e-06 
## Fst10 passed    0.02235 7.38e-06 
## Fst11 passed    0.03803 8.72e-06 
## Fst12 passed    0.33025 5.23e-05 
## Fst13 passed    0.21554 3.15e-05 
## Fst14 passed    0.03949 7.62e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 2435.216 1817.289 2267.104 2030.316 2817.245 3667.422 2762.269 4003.063 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 2277.167 2165.847 2566.946 3135.451 3742.462 3684.990
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                Fst1        Fst2        Fst3         Fst4          Fst5
## Lag 0    1.00000000  1.00000000  1.00000000  1.000000000  1.0000000000
## Lag 10   0.23848071  0.32619571  0.27337901  0.232617735  0.1576148586
## Lag 50   0.03670575  0.06808487  0.04082410  0.060792825  0.0358401141
## Lag 100  0.02312252  0.00943782  0.01247414  0.030748092  0.0002886658
## Lag 500 -0.01071359 -0.01240479 -0.03799974 -0.004012279 -0.0020175678
##                Fst6         Fst7        Fst8        Fst9       Fst10
## Lag 0    1.00000000  1.000000000  1.00000000 1.000000000  1.00000000
## Lag 10   0.10129959  0.196922426  0.11053544 0.233462457  0.29129530
## Lag 50   0.01421689 -0.008022926  0.01753971 0.064882453  0.05023543
## Lag 100 -0.02237282  0.002406781 -0.00415637 0.014518729 -0.02252940
## Lag 500 -0.02284059  0.008862334 -0.02483857 0.001061008  0.02246602
##                Fst11         Fst12        Fst13         Fst14
## Lag 0    1.000000000  1.0000000000 1.0000000000  1.0000000000
## Lag 10   0.240758663  0.1319666496 0.1139277166  0.1512162610
## Lag 50   0.016270535  0.0008020016 0.0003018905  0.0180409642
## Lag 100 -0.005932871  0.0123687483 0.0019310575  0.0032779431
## Lag 500  0.004242687 -0.0127916319 0.0037789503 -0.0009754332
levelplot(t(autocorr.diag(chain)))

#plot(chain)
#chain checked visually not shown here to save file size

2R corr

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/1.corr.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed          1      0.1567 
## Fst2  passed          1      0.2439 
## Fst3  passed          1      0.1189 
## Fst4  passed          1      0.2483 
## Fst5  passed          1      0.3646 
## Fst6  passed          1      0.5694 
## Fst7  passed          1      0.1652 
## Fst8  passed          1      0.4147 
## Fst9  passed       2001      0.0887 
## Fst10 passed          1      0.2128 
## Fst11 passed          1      0.1100 
## Fst12 passed          1      0.7656 
## Fst13 passed          1      0.3387 
## Fst14 passed          1      0.2939 
##                                 
##       Halfwidth Mean   Halfwidth
##       test                      
## Fst1  passed    0.0588 6.23e-06 
## Fst2  passed    0.0250 3.60e-06 
## Fst3  passed    0.0377 4.90e-06 
## Fst4  passed    0.0300 4.00e-06 
## Fst5  passed    0.0351 5.89e-06 
## Fst6  passed    0.0285 5.39e-06 
## Fst7  passed    0.0442 4.64e-06 
## Fst8  passed    0.0566 4.99e-06 
## Fst9  passed    0.0202 4.09e-06 
## Fst10 passed    0.0231 3.35e-06 
## Fst11 passed    0.0593 5.64e-06 
## Fst12 passed    0.4503 2.73e-05 
## Fst13 passed    0.1098 1.03e-05 
## Fst14 passed    0.0288 3.73e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 3555.914 3489.981 3078.990 3794.559 4181.377 4041.699 3797.493 3752.406 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 2771.708 2754.915 3380.949 4043.280 4108.191 3490.012
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                Fst1          Fst2          Fst3         Fst4         Fst5
## Lag 0   1.000000000  1.0000000000  1.0000000000  1.000000000  1.000000000
## Lag 10  0.168587874  0.1776651791  0.1958047181  0.136870352  0.088962805
## Lag 50  0.001240395 -0.0008948971 -0.0007362333 -0.003955262 -0.009919434
## Lag 100 0.009627360  0.0118690405  0.0267553704 -0.016893761  0.000891851
## Lag 500 0.024846297 -0.0230061371 -0.0078393392  0.004196835  0.011274971
##                  Fst6         Fst7         Fst8       Fst9        Fst10
## Lag 0    1.0000000000  1.000000000  1.000000000 1.00000000  1.000000000
## Lag 10   0.1057890229  0.136491167  0.142346911 0.19956141  0.242810666
## Lag 50   0.0017418407 -0.003290712 -0.005268878 0.01071085  0.023723491
## Lag 100 -0.0005252987 -0.001370500  0.002806994 0.01263903 -0.014119630
## Lag 500 -0.0177391215  0.001385723 -0.016114454 0.01760198  0.009257047
##                Fst11        Fst12        Fst13        Fst14
## Lag 0    1.000000000  1.000000000  1.000000000  1.000000000
## Lag 10   0.170927523  0.105595664  0.097714652  0.138572118
## Lag 50   0.020972656  0.009047115  0.007845143 -0.014795044
## Lag 100 -0.001332428 -0.004845484 -0.014368449  0.005944289
## Lag 500  0.010890092 -0.012095286  0.010810719  0.008017509
levelplot(t(autocorr.diag(chain)))

#plot(chain)
#chain checked visually not shown here to save file size

2L corr

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/2.corr.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed       1         0.239  
## Fst2  passed       1         0.743  
## Fst3  passed       1         0.779  
## Fst4  passed       1         0.780  
## Fst5  passed       1         0.287  
## Fst6  passed       1         0.452  
## Fst7  passed       1         0.520  
## Fst8  passed       1         0.659  
## Fst9  passed       1         0.418  
## Fst10 passed       1         0.178  
## Fst11 passed       1         0.388  
## Fst12 passed       1         0.784  
## Fst13 passed       1         0.611  
## Fst14 passed       1         0.705  
##                                 
##       Halfwidth Mean   Halfwidth
##       test                      
## Fst1  passed    0.1212 1.37e-05 
## Fst2  passed    0.0905 1.08e-05 
## Fst3  passed    0.0897 1.02e-05 
## Fst4  passed    0.0373 6.40e-06 
## Fst5  passed    0.0262 7.65e-06 
## Fst6  passed    0.0290 6.88e-06 
## Fst7  passed    0.0347 5.09e-06 
## Fst8  passed    0.0578 6.41e-06 
## Fst9  passed    0.0150 3.45e-06 
## Fst10 passed    0.0482 7.43e-06 
## Fst11 passed    0.0721 7.97e-06 
## Fst12 passed    0.4151 3.25e-05 
## Fst13 passed    0.1417 1.62e-05 
## Fst14 passed    0.0434 6.22e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 3226.260 3420.290 3694.200 3046.744 3090.305 3678.611 3594.275 3669.967 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 2500.083 2510.460 3348.354 4039.824 3669.919 3246.245
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                 Fst1         Fst2        Fst3         Fst4        Fst5
## Lag 0    1.000000000  1.000000000  1.00000000  1.000000000  1.00000000
## Lag 10   0.170934394  0.164179861  0.18056097  0.177101555  0.14523007
## Lag 50  -0.002521939  0.003936389  0.01906813  0.015088535  0.01485925
## Lag 100 -0.010875784 -0.009038200 -0.03276034 -0.002962940 -0.01442010
## Lag 500  0.013507290  0.021517275 -0.01285042 -0.003947699  0.03271510
##                  Fst6        Fst7         Fst8         Fst9        Fst10
## Lag 0    1.0000000000 1.000000000 1.0000000000  1.000000000  1.000000000
## Lag 10   0.1237973829 0.163370610 0.0977194089  0.307213569  0.218251754
## Lag 50   0.0385630512 0.017147150 0.0002478026 -0.005699051  0.041030438
## Lag 100 -0.0281433267 0.006047292 0.0079196634 -0.011739857 -0.022107570
## Lag 500  0.0005123491 0.004855602 0.0021764374  0.017230558  0.001389491
##               Fst11        Fst12        Fst13        Fst14
## Lag 0    1.00000000  1.000000000  1.000000000  1.000000000
## Lag 10   0.17619022  0.106018412  0.153217960  0.212482138
## Lag 50  -0.01410351 -0.006182297 -0.005255178 -0.012968481
## Lag 100  0.01366072 -0.012085173  0.016417243  0.009675629
## Lag 500  0.01322821  0.016207286 -0.001168821  0.000782476
levelplot(t(autocorr.diag(chain)))

#plot(chain)
#chain checked visually not shown here to save file size

3R corr

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/3.corr.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed         1       0.2127 
## Fst2  passed         1       0.4671 
## Fst3  passed         1       0.9548 
## Fst4  passed         1       0.7915 
## Fst5  passed         1       0.8479 
## Fst6  passed         1       0.8126 
## Fst7  passed       501       0.0632 
## Fst8  passed         1       0.3070 
## Fst9  passed         1       0.0660 
## Fst10 passed         1       0.6954 
## Fst11 passed         1       0.5585 
## Fst12 passed         1       0.3304 
## Fst13 passed       501       0.0894 
## Fst14 passed         1       0.4378 
##                                  
##       Halfwidth Mean    Halfwidth
##       test                       
## Fst1  passed    0.02006 3.62e-06 
## Fst2  passed    0.00597 1.97e-06 
## Fst3  passed    0.00996 2.36e-06 
## Fst4  passed    0.01813 2.91e-06 
## Fst5  passed    0.02195 4.99e-06 
## Fst6  passed    0.01989 4.90e-06 
## Fst7  passed    0.01849 2.94e-06 
## Fst8  passed    0.05889 5.65e-06 
## Fst9  passed    0.01058 2.11e-06 
## Fst10 passed    0.01148 2.12e-06 
## Fst11 passed    0.03749 4.00e-06 
## Fst12 passed    0.25806 2.38e-05 
## Fst13 passed    0.07877 9.06e-06 
## Fst14 passed    0.01855 2.87e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 3081.572 2932.849 2969.362 4012.032 4317.203 3787.457 3716.384 3670.020 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 2821.773 2845.133 3939.303 3949.144 3863.843 3676.380
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##               Fst1         Fst2         Fst3         Fst4          Fst5
## Lag 0   1.00000000  1.000000000  1.000000000  1.000000000  1.0000000000
## Lag 10  0.19375816  0.167346557  0.207544154  0.109429986  0.0474377754
## Lag 50  0.03370195  0.033972855 -0.001632805  0.009671385 -0.0005616828
## Lag 100 0.01487360  0.009856599 -0.003109365 -0.021745536 -0.0002144461
## Lag 500 0.01180906 -0.009080761 -0.014157115  0.015293470  0.0005588691
##                  Fst6         Fst7         Fst8         Fst9        Fst10
## Lag 0    1.000000e+00  1.000000000  1.000000000  1.000000000  1.000000000
## Lag 10   1.045221e-01  0.147069040  0.119797388  0.198983767  0.239587439
## Lag 50   4.811015e-03  0.001326572 -0.006603822  0.009194855  0.017563612
## Lag 100 -9.855211e-05  0.031640313  0.022895591 -0.002896565 -0.006168659
## Lag 500 -1.802687e-02 -0.003443671 -0.004019166  0.019565233 -0.012704552
##                Fst11       Fst12         Fst13        Fst14
## Lag 0    1.000000000  1.00000000  1.0000000000  1.000000000
## Lag 10   0.118458166  0.07730739  0.1279820004  0.117988193
## Lag 50   0.018062236 -0.01281953 -0.0121188225  0.010485454
## Lag 100 -0.012906869  0.01055783 -0.0001704971  0.006284584
## Lag 500 -0.006395941 -0.01635416  0.0140383422 -0.010687296
levelplot(t(autocorr.diag(chain)))

#plot(chain)
#chain checked visually not shown here to save file size

3L corr

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/4.corr.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed       1         0.866  
## Fst2  passed       1         0.524  
## Fst3  passed       1         0.384  
## Fst4  passed       1         0.177  
## Fst5  passed       1         0.435  
## Fst6  passed       1         0.697  
## Fst7  passed       1         0.565  
## Fst8  passed       1         0.113  
## Fst9  passed       1         0.692  
## Fst10 passed       1         0.943  
## Fst11 passed       1         0.979  
## Fst12 passed       1         0.411  
## Fst13 passed       1         0.909  
## Fst14 passed       1         0.154  
##                                  
##       Halfwidth Mean    Halfwidth
##       test                       
## Fst1  passed    0.02212 4.36e-06 
## Fst2  passed    0.00671 2.18e-06 
## Fst3  passed    0.01154 2.70e-06 
## Fst4  passed    0.01365 2.99e-06 
## Fst5  passed    0.01445 4.88e-06 
## Fst6  passed    0.01326 4.45e-06 
## Fst7  passed    0.01860 3.30e-06 
## Fst8  passed    0.05435 6.26e-06 
## Fst9  passed    0.00795 2.00e-06 
## Fst10 passed    0.00809 2.11e-06 
## Fst11 passed    0.03603 3.86e-06 
## Fst12 passed    0.24694 2.53e-05 
## Fst13 passed    0.08336 9.99e-06 
## Fst14 passed    0.01787 3.13e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 3133.664 3540.840 3486.318 3667.896 4416.309 4602.765 3646.325 3580.975 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 3161.739 2811.748 5136.474 4246.270 4267.666 3771.724
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                 Fst1          Fst2        Fst3         Fst4         Fst5
## Lag 0    1.000000000  1.0000000000  1.00000000  1.000000000  1.000000000
## Lag 10   0.176669693  0.1706508603  0.16257734  0.127317362  0.061787912
## Lag 50   0.006717724 -0.0092433436 -0.02778312 -0.014458246 -0.018423721
## Lag 100  0.013742640  0.0108210471 -0.00543404  0.004961475  0.004324964
## Lag 500 -0.009028767  0.0004345302 -0.01489376  0.013593804 -0.009199619
##                 Fst6         Fst7         Fst8         Fst9        Fst10
## Lag 0    1.000000000  1.000000000  1.000000000  1.000000000  1.000000000
## Lag 10   0.046827318  0.156365576  0.116728493  0.192613908  0.236817281
## Lag 50   0.004556912  0.025176859 -0.021603835  0.012821565 -0.002123151
## Lag 100 -0.011279939 -0.015205307  0.002252461 -0.028783365  0.012455142
## Lag 500  0.010375905  0.009786558 -0.019529180 -0.003098394 -0.011591084
##               Fst11        Fst12        Fst13         Fst14
## Lag 0    1.00000000  1.000000000  1.000000000  1.000000e+00
## Lag 10   0.14537377  0.081318520  0.078821549  1.398307e-01
## Lag 50  -0.02045055  0.021264384  0.004785627  8.478993e-05
## Lag 100 -0.04199109 -0.008237217  0.018261106 -6.366198e-03
## Lag 500  0.01762633  0.007047980 -0.004627486 -9.497448e-03
#levelplot(t(autocorr.diag(chain)))
#plot(chain)
#chain checked visually not shown here to save file size

X corr

#read in trace file
chain <- read.table("~/Desktop/anoph.phase2/bayescenv.results/5.corr.sel" , header = TRUE)
chain <- mcmc(chain , thin = 10) #Adapt thin to its actual value (10 is the default)
chain<-chain[,3:ncol(chain)]
heidel.diag(chain)             #To test for convergence
##                                     
##       Stationarity start     p-value
##       test         iteration        
## Fst1  passed       1         0.801  
## Fst2  passed       1         0.537  
## Fst3  passed       1         0.844  
## Fst4  passed       1         0.927  
## Fst5  passed       1         0.850  
## Fst6  passed       1         0.307  
## Fst7  passed       1         0.721  
## Fst8  passed       1         0.494  
## Fst9  passed       1         0.514  
## Fst10 passed       1         0.164  
## Fst11 passed       1         0.893  
## Fst12 passed       1         0.586  
## Fst13 passed       1         0.133  
## Fst14 passed       1         0.930  
##                                 
##       Halfwidth Mean   Halfwidth
##       test                      
## Fst1  passed    0.0498 1.35e-05 
## Fst2  passed    0.0134 5.07e-06 
## Fst3  passed    0.0180 6.27e-06 
## Fst4  passed    0.0140 5.06e-06 
## Fst5  passed    0.0190 7.47e-06 
## Fst6  passed    0.0192 7.10e-06 
## Fst7  passed    0.0105 3.54e-06 
## Fst8  passed    0.0706 9.77e-06 
## Fst9  passed    0.0177 4.63e-06 
## Fst10 passed    0.0177 5.04e-06 
## Fst11 passed    0.0418 8.49e-06 
## Fst12 passed    0.3076 5.03e-05 
## Fst13 passed    0.2146 3.29e-05 
## Fst14 passed    0.0435 8.75e-06
effectiveSize(chain)           #To compute effective sample size
##     Fst1     Fst2     Fst3     Fst4     Fst5     Fst6     Fst7     Fst8 
## 2185.957 2552.002 2199.258 2821.450 4084.059 4406.382 3533.534 4401.892 
##     Fst9    Fst10    Fst11    Fst12    Fst13    Fst14 
## 3293.725 2874.069 3115.245 3510.468 3407.479 3222.053
plot(effectiveSize(chain), xaxt='n', ylim=c(0,max(effectiveSize(chain))))+ abline(h=1000, col="red")+ axis(1, at=c(1:length(effectiveSize(chain))), labels=colnames(chain))

## numeric(0)
autocorr.diag(chain)           #To look for auto-correlation
##                Fst1         Fst2         Fst3        Fst4         Fst5
## Lag 0    1.00000000  1.000000000  1.000000000 1.000000000  1.000000000
## Lag 10   0.25873801  0.244915071  0.282993880 0.153944842  0.077058807
## Lag 50   0.03310360  0.041129280  0.024821543 0.040250654 -0.006296514
## Lag 100 -0.01400169 -0.040825265 -0.009632776 0.006806845 -0.001445193
## Lag 500  0.02547378  0.003461243 -0.009982131 0.005070720 -0.027701370
##                 Fst6          Fst7         Fst8         Fst9      Fst10
## Lag 0    1.000000000  1.0000000000  1.000000000  1.000000000 1.00000000
## Lag 10   0.062908716  0.1716533007  0.063416399  0.185537830 0.17834904
## Lag 50   0.019762112  0.0008813034 -0.021527492  0.001000342 0.01164169
## Lag 100 -0.006048685  0.0159285506 -0.005635439 -0.016699548 0.01492923
## Lag 500  0.009029047 -0.0010731644 -0.026589240  0.040094760 0.01272903
##                Fst11        Fst12        Fst13        Fst14
## Lag 0   1.0000000000  1.000000000  1.000000000  1.000000000
## Lag 10  0.2026575072  0.123638023  0.150450850  0.137074433
## Lag 50  0.0006123029 -0.007167739  0.014285323  0.033191547
## Lag 100 0.0066701308  0.011783190  0.019789047 -0.008118197
## Lag 500 0.0088944235  0.002384724 -0.003072994 -0.016617467
levelplot(t(autocorr.diag(chain)))

#plot(chain)
#chain checked visually not shown here to save file size